Interdisciplinary Fundamental Concepts in STEM: Solid State Physics and COVID-19 Pandemic Evolution
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Abstract
The rapid development of computer-aided design tools, such as MATLAB or Octave, or Mathematica enabled students to solve many complicated problems focusing less on underlying STEM-related concepts that are interdisciplinary. Therefore, it is important to demonstrate how it could be done using specific examples that could be linked to different subjects or even to their everyday life experience. This paper reports on using the COVID-19 pandemic evolution model (Shur, 2022) in my class on the physics of advanced semiconductors devices. I use this model to show how the concepts, such as the Born-Oppenheimer approximation and Fermi-Dirac distribution function could be used in a completely different STEM field. In solid-state physics, the Born-Oppenheimer approximation is used to separate rapid electronic motion, relevant to the electronic states and much slower nuclei motion (since nuclei are thousands of times heavier than electrons). Likewise, the COVID-19 model uses a relatively fast pandemic evolution growth or decay constant, a slow function of time itself. In solid-state physics, the Fermi-Dirac distribution function describes the transition from the occupied electronic states to empty electron states with the temperature determining the transition interval. The COVID-19 model uses the generalized Fermi-distribution function to describe the mitigation measures that determine the transition from a high to a lower infection rate. A more accurate COVID-19 evolution model requires a generalized Fermi-Dirac function that accounts for a slow variation of the effect of the mitigation measures with time. In turn, this generalization could be used in solid-state physics to describe the electron temperature increase in the electric field.
Keywords
Pandemics, COVID-19 Pandemic evolution, Fermi-Dirac Smoothed function
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PDFDOI: https://doi.org/10.46328/ijonest.160
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International Journal on Engineering, Science and Technology (IJonEST)-ISSN: 2642-4088
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